Filtering Noise From Volatility
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Filtering Noise From Volatility Portfolio Management Risk Analysis Et Al
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Author : Alexander Izmailov
language : en
Publisher:
Release Date : 2014
Filtering Noise From Volatility Portfolio Management Risk Analysis Et Al written by Alexander Izmailov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Demonstration of the omnipresence of noise in volatilities of returns of financial instruments.Demonstration that more than 30% of SP500 securities can have percentage change in volatility of more than 10% as a result of noise filtering.In our white paper “Filtering Noise From Correlation Matrices” we have described in detail the source of noise in the correlation matrices. It is natural to assume that the same noise is present in the covariance matrix too. In particular, variances (diagonal elements of the covariance matrix - squares of volatility) contain noise as well. Our noise-filtering procedure is capable of reducing noise contained in variances in a coherent way with the noise reduction in the initial correlation matrix.
Filtering Noise From Volatility
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Author : Alexander Izmailov
language : en
Publisher:
Release Date : 2014
Filtering Noise From Volatility written by Alexander Izmailov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Alexander Izmailov, Ph.D (theoretical physics) and Brian Shay, Ph.D (mathematics), of Market Memory Trading, L.L.C., present in a series of nine (9) white papers, aspects of a revolutionary advance in uncovering hidden dependencies via filtering noise from correlation matrices developed by the New York based company, Market Memory Trading, L.L.C. (MMT). Correlations are quantitative measures of these dependencies and noise filtering increases their accuracy as a decision-making tool, from asset allocation to LIBOR Surveillance and cyber security.“FILTERING NOISE FROM VOLATILITY.” White Paper 5, dated March 26, 2013, provides a demonstration of the omnipresence of noise in volatilities of returns of financial instruments; and a demonstration that more than 30% of SP500 securities can have percentage change in volatility of more than 10% as a result of noise filtering. Refer to Appendix A for Complete Series.
A Combined Filtering Approach To High Frequency Volatility Estimation With Mixed Type Microstructure Noises
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Author : Yinfen Tang
language : en
Publisher:
Release Date : 2018
A Combined Filtering Approach To High Frequency Volatility Estimation With Mixed Type Microstructure Noises written by Yinfen Tang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
This paper introduces a solution that combines the Kalman and particle fi lters to the challenging problem of estimating integrated volatility using high-frequency data where the underlying prices are perturbed by a mixture of random noise and price discreteness. An explanation is presented of how the proposed combined filtering approach is able to correct for bias due to this mixed-type microstructure effect. Simulation and empirical studies on the tick-by-tick trade price data for four US stocks in the year 2009 show that our method has clear advantages over existing high-frequency volatility estimation methods.
Inside Volatility Filtering
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Author : Alireza Javaheri
language : en
Publisher: John Wiley & Sons
Release Date : 2015-07-27
Inside Volatility Filtering written by Alireza Javaheri and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-27 with Business & Economics categories.
A new, more accurate take on the classical approach to volatility evaluation Inside Volatility Filtering presents a new approach to volatility estimation, using financial econometrics based on a more accurate estimation of the hidden state. Based on the idea of "filtering", this book lays out a two-step framework involving a Chapman-Kolmogorov prior distribution followed by Bayesian posterior distribution to develop a robust estimation based on all available information. This new second edition includes guidance toward basing estimations on historic option prices instead of stocks, as well as Wiener Chaos Expansions and other spectral approaches. The author's statistical trading strategy has been expanded with more in-depth discussion, and the companion website offers new topical insight, additional models, and extra charts that delve into the profitability of applied model calibration. You'll find a more precise approach to the classical time series and financial econometrics evaluation, with expert advice on turning data into profit. Financial markets do not always behave according to a normal bell curve. Skewness creates uncertainty and surprises, and tarnishes trading performance, but it's not going away. This book shows traders how to work with skewness: how to predict it, estimate its impact, and determine whether the data is presenting a warning to stay away or an opportunity for profit. Base volatility estimations on more accurate data Integrate past observation with Bayesian probability Exploit posterior distribution of the hidden state for optimal estimation Boost trade profitability by utilizing "skewness" opportunities Wall Street is constantly searching for volatility assessment methods that will make their models more accurate, but precise handling of skewness is the key to true accuracy. Inside Volatility Filtering shows you a better way to approach non-normal distributions for more accurate volatility estimation.
Inside Volatility Arbitrage
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Author : Alireza Javaheri
language : en
Publisher: John Wiley & Sons
Release Date : 2011-08-24
Inside Volatility Arbitrage written by Alireza Javaheri and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-24 with Business & Economics categories.
Today?s traders want to know when volatility is a sign that the sky is falling (and they should stay out of the market), and when it is a sign of a possible trading opportunity. Inside Volatility Arbitrage can help them do this. Author and financial expert Alireza Javaheri uses the classic approach to evaluating volatility -- time series and financial econometrics -- in a way that he believes is superior to methods presently used by market participants. He also suggests that there may be "skewness" trading opportunities that can be used to trade the markets more profitably. Filled with in-depth insight and expert advice, Inside Volatility Arbitrage will help traders discover when "skewness" may present valuable trading opportunities as well as why it can be so profitable.
Stochastic Filtering With Applications In Finance
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Author : Ramaprasad Bhar
language : en
Publisher: World Scientific
Release Date : 2010
Stochastic Filtering With Applications In Finance written by Ramaprasad Bhar and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Business & Economics categories.
This book provides a comprehensive account of stochastic filtering as a modeling tool in finance and economics. It aims to present this very important tool with a view to making it more popular among researchers in the disciplines of finance and economics. It is not intended to give a complete mathematical treatment of different stochastic filtering approaches, but rather to describe them in simple terms and illustrate their application with real historical data for problems normally encountered in these disciplines. Beyond laying out the steps to be implemented, the steps are demonstrated in the context of different market segments. Although no prior knowledge in this area is required, the reader is expected to have knowledge of probability theory as well as a general mathematical aptitude. Its simple presentation of complex algorithms required to solve modeling problems in increasingly sophisticated financial markets makes this book particularly valuable as a reference for graduate students and researchers interested in the field. Furthermore, it analyses the model estimation results in the context of the market and contrasts these with contemporary research publications. It is also suitable for use as a text for graduate level courses on stochastic modeling.
Liquidity Based Estimation Of Spot Volatility Under Microstructure Noise
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Author : Oliver Grothe
language : en
Publisher:
Release Date : 2010
Liquidity Based Estimation Of Spot Volatility Under Microstructure Noise written by Oliver Grothe and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with categories.
Recent literature on realized volatility suggests that the observed price process of an asset may be decomposed into two parts: the unobservable, efficient price process and microstructure noise. In this article we present a methodology to sequentially estimate spot volatility from noisy data by separating these components. We use different liquidity-based measures, traded volume and quoted spread, for the noise variance of single price observations. Nonlinear Kalman filters provide us with sequential estimates of the unobservable price process and its parameters. Our approach is implemented in a continuous-discrete state space model to cope with irregular trading frequencies.
Mathematical And Statistical Methods For Actuarial Sciences And Finance
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Author : Marco Corazza
language : en
Publisher: Springer
Release Date : 2014-08-06
Mathematical And Statistical Methods For Actuarial Sciences And Finance written by Marco Corazza and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-08-06 with Mathematics categories.
The interaction between mathematicians and statisticians has been shown to be an effective approach for dealing with actuarial, insurance and financial problems, both from an academic perspective and from an operative one. The collection of original papers presented in this volume pursues precisely this purpose. It covers a wide variety of subjects in actuarial, insurance and finance fields, all treated in the light of the successful cooperation between the above two quantitative approaches. The papers published in this volume present theoretical and methodological contributions and their applications to real contexts. With respect to the theoretical and methodological contributions, some of the considered areas of investigation are: actuarial models; alternative testing approaches; behavioral finance; clustering techniques; coherent and non-coherent risk measures; credit scoring approaches; data envelopment analysis; dynamic stochastic programming; financial contagion models; financial ratios; intelligent financial trading systems; mixture normality approaches; Monte Carlo-based methods; multicriteria methods; nonlinear parameter estimation techniques; nonlinear threshold models; particle swarm optimization; performance measures; portfolio optimization; pricing methods for structured and non-structured derivatives; risk management; skewed distribution analysis; solvency analysis; stochastic actuarial valuation methods; variable selection models; time series analysis tools. As regards the applications, they are related to real problems associated, among the others, to: banks; collateralized fund obligations; credit portfolios; defined benefit pension plans; double-indexed pension annuities; efficient-market hypothesis; exchange markets; financial time series; firms; hedge funds; non-life insurance companies; returns distributions; socially responsible mutual funds; unit-linked contracts. This book is aimed at academics, Ph.D. students, practitioners, professionals and researchers. But it will also be of interest to readers with some quantitative background knowledge.
Dramatically Improved Portfolio Optimization Results With Noise Filtered Covariance
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Author : Alexander Izmailov
language : en
Publisher:
Release Date : 2014
Dramatically Improved Portfolio Optimization Results With Noise Filtered Covariance written by Alexander Izmailov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with categories.
Demonstration that in-sample Markowitz type mean-variance optimization, carried out with noise filtered covariance matrices, results in asset allocation that leads to 2-3 times increase of the Sharpe ratio compared to the same optimization carried out without noise filtering.Demonstration of 2-3 times increase of the Sharpe ratio due to asset allocation obtained via optimization, carried out with noise filtered covariance matrices, for two possible optimization scenarios - maximization of portfolio return at a fixed volatility and minimization of portfolio volatility at a fixed return.
Volatility Noise
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Author : Michael Hofmann
language : en
Publisher:
Release Date : 2018
Volatility Noise written by Michael Hofmann and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018 with categories.
This study shows that fitting errors of equity-option-implied volatility surfaces are informative about intermediary frictions. For each stock and day, we quantify the goodness of fit between the observed implied volatilities of all available options and the corresponding estimates from OptionMetrics' smoothed volatility surface using root-mean-square errors. In the cross-section of stocks, this error metric increases in idiosyncratic stock volatility and several measures of option and stock illiquidity. Based on these insights, we propose an overarching measure for intermediary frictions given by the value-weighted average of the stock-specific fitting errors. This measure of volatility noise peaks during episodes of market distress and exhibits sensible correlations to standard economic state variables like the market return, the TED spread, or the VIX. Moreover, we uncover a close link between volatility noise and the constraints on intermediary equity and debt. Finally, our asset pricing results indicate that volatility noise is informative for the cross-sectional variation in expected returns beyond the equity option market. In particular, we find volatility noise to constitute a priced risk factor in returns of stocks as well as equity and bond mutual funds.